中文标题#
一种隐私保护语言建模方法的研究
英文标题#
A Study of Privacy-preserving Language Modeling Approaches
中文摘要#
最近在语言建模方面的进展增加了它们在各种应用和领域中的使用。 语言模型通常在敏感数据上进行训练，可能在隐私攻击期间记住并泄露这些信息，引发了对保护个人隐私权的关注。 在语言模型中保护隐私已成为一个关键的研究领域，因为隐私是基本的人类权利之一。 尽管其重要性，人们对这些语言模型所具有的隐私风险程度以及如何减轻风险的理解仍然有限。 本研究通过提供对隐私保护语言建模方法的全面研究来解决这个问题。 本研究对这些方法进行了深入概述，强调了它们的优势，并探讨了它们的局限性。 本研究的结果有助于隐私保护语言建模的持续研究，提供了有价值的见解并指明了未来的研究方向。
英文摘要#
Recent developments in language modeling have increased their use in various applications and domains. Language models, often trained on sensitive data, can memorize and disclose this information during privacy attacks, raising concerns about protecting individuals' privacy rights. Preserving privacy in language models has become a crucial area of research, as privacy is one of the fundamental human rights. Despite its significance, understanding of how much privacy risk these language models possess and how it can be mitigated is still limited. This research addresses this by providing a comprehensive study of the privacy-preserving language modeling approaches. This study gives an in-depth overview of these approaches, highlights their strengths, and investigates their limitations. The outcomes of this study contribute to the ongoing research on privacy-preserving language modeling, providing valuable insights and outlining future research directions.
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